code: def test_distributed_tensor_gatherer(self):
      # Simulate a result with a dataset of size 21, 4 processes and chunks of lengths 2, 3, 1
      world_size = 4
      num_samples = 21
      input_indices = [
          [0, 1, 6, 7, 12, 13, 18, 19],
          [2, 3, 4, 8, 9, 10, 14, 15, 16, 20, 0, 1],
          [5, 11, 17, 2],
      ]
  
      predictions = np.random.normal(size=(num_samples, 13))
      gatherer = DistributedTensorGatherer(
          world_size=world_size, num_samples=num_samples
      )
      for indices in input_indices:
          gatherer.add_arrays(predictions[indices])
      result = gatherer.finalize()
      self.assertTrue(np.array_equal(result, predictions))
  
      # With nested tensors
      gatherer = DistributedTensorGatherer(
          world_size=world_size, num_samples=num_samples
      )
      for indices in input_indices:
          gatherer.add_arrays(
              [
                  predictions[indices],
                  [predictions[indices], predictions[indices]],
              ]
          )
      result = gatherer.finalize()
      self.assertTrue(isinstance(result, list))
      self.assertEqual(len(result), 2)
      self.assertTrue(isinstance(result[1], list))
      self.assertEqual(len(result[1]), 2)
      self.assertTrue(np.array_equal(result[0], predictions))
      self.assertTrue(np.array_equal(result[1][0], predictions))
      self.assertTrue(np.array_equal(result[1][1], predictions))
  
imports: import copy
  import unittest
  import numpy
  from transformers.data.data_collator import default_data_collator
  from transformers.testing_utils import require_accelerate, require_torch
  from transformers.trainer_utils import RemoveColumnsCollator, find_executable_batch_size
  from transformers.utils import is_torch_available
label_desc: Use image node_id 1 for calling the TrainerUtilsTest obj's underlying member method code with example usage: obj.test_distributed_tensor_gatherer() without return types